Early Vision and Visual System Development Dr. James A. Bednar jbednar@inf.ed.ac.uk http://homepages.inf.ed.ac.uk/jbednar CNV Spring 2008: Vision background 1
Studying the visual system (1) The visual system can be (and is) studied using many different techniques. In this course we will consider: Psychophysics What is the level of human visual performance under various different conditions? Anatomy Where are the visual system parts located, and what do they look like? Gross anatomy What do the visual system organs and tissues look like, and how are they connected? Histology What cellular and subcellular structures can be seen under a microscope? CNV Spring 2008: Vision background 2
Studying the visual system (2) Physiology What is the behavior of the component parts of the visual system? Electrophysiology What is the electrical behavior of neurons, measured with an electrode? Imaging What is the behavior of a large area of the nervous system? Genetics Which genes control visual system development and function, and what do they do? CNV Spring 2008: Vision background 3
Electromagnetic spectrum (From web) Start with the physics: visible portion is small, but provides much information about biologically relevant stimuli CNV Spring 2008: Vision background 4
Cone spectral sensitivities (Dowling, 1987) Somehow we make do with sampling the visible range of wavelengths at only three points (3 cone types) CNV Spring 2008: Vision background 5
Early visual pathways � 1994 L. Kibiuk c Eye LGN V1 Signals travel from retina, to LGN, then to primary visual cortex CNV Spring 2008: Vision background 6
Higher areas • Many higher areas beyond V1 • Selective for faces, motion, etc. • Not as well understood Macaque visual areas (Van Essen et al. 1992) CNV Spring 2008: Vision background 7
Circuit diagram Connections between macaque visual areas (Van Essen et al. 1992) A bit messy! (Yet still just a start.) CNV Spring 2008: Vision background 8
Image formation (Kandel et al. 1991) Fixed Adjustable Sampling Camera: lens shape focal length uniform Eye: focal length lens shape higher at fovea CNV Spring 2008: Vision background 9
Visual fields Right eye right Right LGN CMVC figure 2.1 Primary Visual field visual cortex Left LGN (V1) Optic chiasm left Left eye • Each eye sees partially overlapping areas • Inputs from opposite hemifield cross over at chiasm CNV Spring 2008: Vision background 10
Retinotopic map Mapping of visual field in macaque monkey Blasdel and Campbell 2001 • Visual field is mapped onto cortical surface • Fovea is overrepresented CNV Spring 2008: Vision background 11
Effect of foveation (From omni.isr.ist.utl.pt) Smaller, tightly packed cones in the fovea give much higher resolution CNV Spring 2008: Vision background 12
Retinal surface Rods Cones (Wandell 1995) Cones in fovea Cones and rods in periphery • No rods in fovea • Cones are larger in periphery • Cone spacing also increases, with gaps filled by rods CNV Spring 2008: Vision background 13
Blue cones in fovea (From web) Blue cones are a bit larger, rarer CNV Spring 2008: Vision background 14
Retinal circuits (Kandel et al. 1991) Rod pathway Rod, rod bipolar cell, ganglion cell Cone pathway Cone, bipolar cell, ganglion cell CNV Spring 2008: Vision background 15
LGN layers (Hubel & Wiesel 1977) Multiple aligned representations of visual field in the LGN for different eyes and cell types CNV Spring 2008: Vision background 16
V1 layers (From webvision.umh.es) Multiple layers of cells in V1 Brodmann numbering CNV Spring 2008: Vision background 17
Retinal/LGN cell response types Types of receptive fields based on responses to light: in center in surround On-center excited inhibited Off-center inhibited excited CNV Spring 2008: Vision background 18
Color-opponent retinal/LGN cells (From webexhibits.org) Red/Green cells: (+R,-G), (-R,+G), (+G,-R), (-G,+R) Blue/Yellow cells: (+B,-Y); others? Error: light arrows in the figure are backwards! CNV Spring 2008: Vision background 19
V1 simple cell responses 2-lobe simple 3-lobe simple cell cell Starting in V1, only oriented patterns will cause any significant response Simple cells: pattern preferences can be plotted as above CNV Spring 2008: Vision background 20
V1 complex cell responses (Same response to all these patterns) Complex cells are also orientation selective, but have responses invariant to phase Can’t measure complex RFs using pixel-based correlations CNV Spring 2008: Vision background 21
Spatiotemporal receptive fields • Neurons are selective for multiple stimulus dimensions at once • Typically prefer lines moving in direction perpendicular to orientation preference (Cat V1; DeAngelis et al. 1999) CNV Spring 2008: Vision background 22
Contrast perception 0% 3% 6% 12% 25% 100% • Humans can detect patterns over a huge contrast range • In the laboratory, increasing contrast above a fairly low value does not aid detection • See 2AFC (two-alternative forced-choice) test in google and ROC (Receiver Operating Characteristic) in Wikipedia for more info on how such tests work CNV Spring 2008: Vision background 23
Contrast-invariant tuning • Single-cell tuning curves are typically Gaussian • 5%, 20%, 80% contrasts shown • Peak response increases, but • Tuning width changes little (Sclar & Freeman 1982) CNV Spring 2008: Vision background 24
Definitions of contrast Luminance (luminosity): Physical amount of light Contrast: Luminance relative to background levels to which the visual system has become adapted Contrast is a fuzzy concept – clear only in special cases: Weber contrast (e.g. a tiny spot on uniform background) C = Lmax − Lmin Lmin Michelson contrast (e.g. a full-field sine grating): C = Lmax − Lmin Lmax + Lmin CNV Spring 2008: Vision background 25
Measuring cortical maps CMVC figure 2.3 • Surface reflectance (or voltage-sensitive-dye emission) changes with activity • Measured with optical imaging • Preferences computed as correlation between measurement and input CNV Spring 2008: Vision background 26
Orientation map in V1 Adult monkey; Blasdel 1992; 5mm • Overall organization is retinotopic • Local patches prefer different orientations CNV Spring 2008: Vision background 27
Ocular dominance map in V1 Macaque; Blasdel 1992 Eye preference map interleaved with orientation CNV Spring 2008: Vision background 28
Direction map in V1 (Adult ferret; Weliky et al. 1996) Direction preference OR/Direction pref. • Local patches prefer different directions • Single-OR patches often subdivided by direction • Other maps: spatial frequency, color CNV Spring 2008: Vision background 29
Cell-level organization Two-photon microscopy: • New technique with cell-level resolution • Can measure a small volume very precisely (Ohki et al. 2005) Rat V1 CNV Spring 2008: Vision background 30
Cell-level organization 2 • Individual cells can be tagged with feature preference • In rat, orientation preferences are random • Random also expected in mouse, squirrel (Ohki et al. 2005) Rat V1 CNV Spring 2008: Vision background 31
Cell-level organization 3 • In cat, validates results from optical imaging • Smooth organization for direction overall • Sharp, well-segregated discontinuities (Ohki et al. 2005) Cat V1 Dir. CNV Spring 2008: Vision background 32
Cell-level organization 4 • Very close match with optical imaging results • Stacking labeled cells from Low-res map all layers shows very strong ordering spatially and in across layers • No significant loss of selectivity in pinwheels Stack of all labeled cells (Ohki et al. 2006) CNV Spring 2008: Vision background 33
Surround modulation 40% 30% 20% 10% Which of the contrasts at left matches the central area? CNV Spring 2008: Vision background 34
Contextual interactions Adjacent line elements interact visually (tilt illusion) Presumably due to lateral or feedback connections at V1 or above CNV Spring 2008: Vision background 35
Lateral connections (Macaque; Gilbert et al. 1990) • Example layer 2/3 pyramidal cell • Patchy every 1mm CNV Spring 2008: Vision background 36
Lateral connections (2.5 mm × 2 mm in tree shrew V1; Bosking et al. 1997) • Connections up to 8mm link to similar preferences • Patchy structure, extend along OR preference CNV Spring 2008: Vision background 37
Feedback connections (Macaque; Angelucci et al. 2002) • Relatively little known about feedback connections • Large number, wide spread • Some appear to be diffuse • Some are patchy and orientation-specific CNV Spring 2008: Vision background 38
Visual development Research questions: • Where does the visual system structure come from? • How much of the architecture is specific to vision? • What influence does the environment have? • How plastic is the system in the adult? Most visual development studies focus on ferrets and cats, whose visual systems are very immature at birth. CNV Spring 2008: Vision background 39
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